290 PrEP Access Navigator (PAN): Creating a comprehensive ‘application cheat sheet’ for the Trillium Drug Program
Bibliographic record
Abstract
Objectives/Goals: To develop a user-friendly tool to simplify the Trillium Drug Program (TDP) application process, addressing barriers for pre-exposure prophylaxis (PrEP) users aged 25 to 64 years. This project may also serve as a scalable model for improving accessibility across other service delivery programs. Methods/Study Population: This study uses the Translational Research Framework to create and refine an online TDP guide. Participants include Ontario residents aged 23 to 64 years. Challenges are identified in the “Understand” phase, while user-driven feedback in the “Act” phase iteratively enhances the tool. Virtual prototyping interviews via Zoom will assess user experience, and error rates will be evaluated by comparing tool suggestions with verified mock scenarios. Twenty participants will test the tool in two iterations: Version 1 from January to February 2025, and Version 2 based on feedback from February to March 2025. Results/Anticipated Results: It is anticipated that participants will encounter fewer barriers to completing the TDP application when using the tool. We expect to see an improvement in user experience by simplifying complex procedures and guiding participants through mock applications with generated scenarios. Data collected from user feedback will highlight specific elements of the tool that require enhancement. Ultimately, we anticipate an increase in successful TDP applications among participants and improvements in accessibility and efficiency of the application process for PrEP users aged 23 to 64 years in Ontario. Discussion/Significance of Impact: The developed tool aims to reduce financial barriers to PrEP access by facilitating successful enrolment in the TDP. The project’s broader impact includes improving health outcomes for underserved communities and contributing to equitable healthcare service delivery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".